Shinypredict

KPI combined with line and bar plots

Interactive plotly line charts tabset layout alt text

KPI number of cases and day to day percent change
Line chart cases by country, selected by drop down menu
Top 10 country rates *10,000 cases

Section including tabs

Dashboard with Maps and tables alt text

Different plot metrics are allocated on each tab
Legend

Dashboard features scripts

Shiny script example different feature (R functions)

Feature	Function	Code
KPI	infoBoxOutput()	fluidRow(
		infoBoxOutput(“Totalrecovered”, width = 3),

		infoBoxOutput(“Totaldeaths”, width = 3),
		infoBoxOutput(“Date”, width = 3)
		),
MAPS	infomap	dataframe %>%
		leaflet() %>%
		addTiles() %>%
		setView(lng = -10, lat = 20, zoom = 3) %>%
		addCircles(lng = ~ Long,
		lat = ~Lat,
		weight = 5,
		radius = ~sqrt(dataframe$Deaths)*1000,

How to plot maps

Maps and how to obtain data from github

Feature	Function	Code
MAPS	infomap	dataframe %>%
		popup = paste0(
		“Country: ”,dataframe$Country,'',dataframe$date,
		“ Province: ”,dataframe$Province,
		“ Confirmed=”,dataframe$Confirmed,
		“ Deaths=”,dataframe$Deaths,
		“ Recovered=”,dataframe$Recovered,
		sep = “ ”
		),
		fillColor = “lightblue”,
		highlightOptions = highlightOptions( weight = 10, color = “red”, fillColor = “green”)
		) %>%
		addLegend( pal = pal_sb, values = dataframe$Deaths,
		position = “bottomleft”,
		title = “Total COVID19 deaths”)})

Github data from open repositories

The data source to produce this dashboard is the following Github repository https://github.com/CSSEGISandData/COVID-19 alt text

Function	Function	Code
Get data daily update	downlopad data from Github	dataframe %>%
		DownloadTheCOVIDData <- function() {}
Update daily data	Check data file for latest download time, it greater than 24h download again min

Github data from open repositories

Script to download data and update daily information for the app

Function	Function	Code
Get daily data	download it from Github	dataframe %>%
		DownloadTheCOVIDData <- function() {
		if(!dir.exists(“data”)){
		dir.create(“data”)
		download.file(
		url = “https://github.com/CSSEGISandData/COVID-19/archive/master.zip”,
		destfile = “data/covid19JH.zip” )
		datapath <- “COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19”
		We need to unzip them and get the three .csv files
		unzip(zipfile = “data/covid19JH.zip”,
		files = paste0(data_path, c(“confirmed_global.csv”,
		“deaths_global.csv”,
		“recovered_global.csv”)),exdir = “data”, junkpaths = T)}

Forecast tab content

Includes next month Covid19 cases forecast and RMSE accuracy measure to assess model performance.

Uses two packages: Tidymodels and Modeltime

1. Tidymodels The tidymodels framework is a collection of packages for modeling and machine learning using tidyverse principles. Tidymodels website

2. Modeltime The tidymodels extension for Time Series Modeling Modeltime website Forecast example using the above package. Models: ARIMA, PROPHET, ETS, SARIMA alt text

Tidymodels overview

Tidymodels allows you to implement a full cycle of models creation and validation alt text

It is a framework designed to:Create and Tune robust models

alt text Compare and work with your models,Develop custom modeling tools

Modeltime overview

Modeltime is designed to combine different time series analysis alt text ARIMA, ETS, Exponential Smoothing

alt text - Also includes PROPHET Facebook model - Streamline models creation - It is part an entire modelling ecosystem

Shiny apps security setup - Avoid SQL injections

In terms of security, we need to avoid SQL injections when creating Shiny forms to enter data We want to avoid users to execute in thb DBMS without having legitimate access to it:

dbGetQuery(conn,paste0(“SELECT * FROM City LIMIT”,input$nrows,“;”))

alt text

input$rows has been put directly into the query

We can avoid such attack by converting first the query into an integer, losing its meaning as a command to the database.

Solution: dbGetQuery(conn,paste0(“SELECT * FROM City LIMIT”,input$nrows,“;”, as.integer(input$nrows)[1],“;”))

Deploying your dashboard to the cloud

This article will show you how to create a shinyapps.io account and deploy your first application to the cloud

Shiny R Studio Server alt text

Thanks

Thank you all for your time

Any questions?

Contat for further details about Shiny:pablo.leonrodenas@nhs.net

Shinypredict

Advanced Shiny dashboards

Takeaways from presentation

Basic Shiny app overview

Basic Shiny app example

Including new features

Wide range of dashboard layouts

Maps example

KPI combined with line and bar plots

Section including tabs

Dashboard features scripts

How to plot maps

Github data from open repositories

Github data from open repositories

Forecast tab content

Tidymodels overview

Modeltime overview

Shiny apps security setup - Avoid SQL injections

Deploying your dashboard to the cloud

Thanks